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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
David L. Aumiller, Michael J. Meholic
Nuclear Science and Engineering | Volume 184 | Number 3 | November 2016 | Pages 453-462
Technical Paper | doi.org/10.13182/NSE16-42
Articles are hosted by Taylor and Francis Online.
COBRA-IE is a three-field subchannel analysis code under development at the Bettis Atomic Power Laboratory. The analysis code is being developed as a general-purpose thermal-hydraulic analysis tool with an emphasis on use in an integrated code system for analyzing postulated large-break loss-of-coolant accidents.
The overall accuracy of programs such as COBRA-IE is tied to the ability to predict void fraction. As such, a comprehensive assessment has been made using one-dimensional void fraction data. The results of this assessment are provided in this paper. The assessment utilizes data from nine different experimental facilities. It includes data from air-water and steam-water facilities, heated flow, adiabatic flow, subcooled boiling, saturated boiling, cocurrent upflow, and cocurrent downflow. Approximately 1100 data points are evaluated and included in this assessment. Overall, COBRA-IE was able to predict the void fraction with an average error (predicted − experimental) of less than 0.04. Plots describing the relationship between the error in the prediction and parameters such as pressure and flow are also provided.